Tolerogenic Vaccination Using IL-12 Gene-Silenced Dendritic Cells for Autoimmune Arthritis (131.9)
Bibliographic record
Abstract
Abstract We have recently demonstrated that dendritic cell (DC)-mediated immune modulation and deviation can be accomplished through RNA interference (RNAi), highlighting the therapeutic potential of RNAi-modified DC as antigen-specific tolerogenic vaccines. To date, an RNAi-based vaccine has not been reported. The current study was designed to develop siRNA-modified DC as antigen-specific, tolerogenic vaccines for prevention and intervention of autoimmune arthritis. Using small interfering RNA (siRNA) that specifically targets IL-12p35 gene (IL-12 siRNA), we have generated a type of DC that exhibits multiple tolerogenic characteristics. Immunization with type II collagen (CII)-pulsed and IL-12 gene-silenced DC (CII-pulsed/gene-silenced DC) resulted in antigen-specific nonresponsiveness in T cell responses. Vaccination with CII-pulsed/gene-silenced DC prevented collagen-induced arthritis (CIA) onset in a murine rheumatoid arthritis model. Furthermore, administration of CII-pulsed/gene-silenced DC was sufficient to inhibit progression of CIA. The therapeutic effects were further evidenced by decreased clinical scores, inhibited inflammatory infiltrates, and suppressed T cell and B cell responses to CII. In conclusion, this study is the first to demonstrate the therapeutic utilization of RNAi-modified DC as antigen-specific tolerogenic vaccines for autoimmune arthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".